{"id":"W2576982189","doi":"10.1016/j.ebiom.2017.02.022","title":"Mining Human Prostate Cancer Datasets: The “camcAPP” Shiny App","year":2017,"lang":"en","type":"article","venue":"EBioMedicine","topic":"Prostate Cancer Treatment and Research","field":"Medicine","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Institute for Cancer Research; University of Toronto","funders":"Academy of Medical Sciences; National Institute for Health and Care Research; Cancer Research UK","keywords":"Prostate cancer; Computer science; Prostatectomy; Annotation; Relevance (law); Cancer; Information retrieval; Bioinformatics; Medicine; Artificial intelligence; Biology; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000464496,0.0001995996,0.0003261693,0.0001018294,0.001042371,0.00008857006,0.0004332245,0.00006379186,0.0005873709],"category_scores_gemma":[0.00009829383,0.0001027526,0.00006039212,0.00012456,0.0008423544,0.0001471425,0.0002011352,0.0002792917,0.00006096841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001181998,"about_ca_system_score_gemma":0.0001616374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009277762,"about_ca_topic_score_gemma":0.0001261151,"domain_scores_codex":[0.9983697,0.00002920657,0.0002448822,0.0003562618,0.0005229866,0.0004769973],"domain_scores_gemma":[0.9982743,0.00004445002,0.0001561968,0.001211076,0.00008220789,0.0002317265],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009587164,0.0003869271,0.2003132,0.0005289059,0.0007893453,0.001120973,0.004528898,7.021387e-7,0.05091318,0.000331765,0.6165954,0.123532],"study_design_scores_gemma":[0.0110759,0.0009784444,0.2553133,0.001142849,0.0004088182,0.00008555374,0.001304331,0.00007830621,0.005969245,0.0002250178,0.7231019,0.0003162425],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8507212,0.0104416,0.00001035065,0.1138152,0.001049922,0.002018608,0.000562786,0.0001747792,0.02120551],"genre_scores_gemma":[0.9717418,0.001054205,0.0001028396,0.001030477,0.001329645,0.0001932911,0.0009067904,0.00003919636,0.02360174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1232157,"threshold_uncertainty_score":0.8017179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05564053333025964,"score_gpt":0.4103770028042905,"score_spread":0.3547364694740308,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}